April 27, 2022
Let’s talk about… Artificial Intelligence and Machine Learning
Joe Cichetto is our VP of Technology and Development at Genuine. He gives us the intel on what these technologies are and how they can help clients save time and effort while being more targeted in the website and marketing experiences.
- What is Artificial Intelligence (AI) and Machine Learning (ML) and what makes ML such an interesting tool for brands?
Artificial Intelligence is a large umbrella term. When we think of AI, we always tend to gravitate towards the human-like robot that is displayed in movies. Machine Learning is a subset of AI, which learns from massive datasets through training, and more recently has even been able to do it on its own.
When we look at modern companies and enterprises, the amount of data exchanged daily is massive, and the benefits of Machine Learning are easily quantifiable. Machine Learning is critical in gaining deep insights into customer psychology and automating areas to reduce manual efforts. Larger amounts of historical data will allow you to determine predictive behaviors which will emerge giving you insights into what key areas of investment you need to make as an organization.
- What can brands gain from utilizing these tools?
One of the best use cases for Machine Learning is the ability to personalize an end-user’s digital experience. This can be anything from content to product offers. Most modern Digital Experience Platforms (DXP) have their own machine learning algorithms tied into their platforms today, and each release cycle they are becoming more sophisticated and customizable to your organization.
Machine Learning can enable marketers to reduce customer acquisition costs by leveraging a real-time dynamic profile created to surface personalized content and information to end-users. This has proven to result in higher conversions and works extremely well in commerce applications where cross-sell and up-sell opportunities exist.
Machine Learning is also very important in terms of automation — there are enormous amounts of manual tasks completed each day. Machine Learning is key in automating these types of tasks because the algorithms can be trained to look for specific patterns and automate how they are handled. A simple example is automating workflow approvals for content, or auto-tagging content. But these automation workflows can be very complex and chained together.
The end-goal is to be able to leverage Machine Learning’s predictive capabilities. This allows fast experimentation with extremely low cost. Imagine being able to determine the success of a large marketing campaign before it even runs? This is what Machine Learning can do with large amounts of trained data.
- Who is doing ML well?
I think Optimizely has done a great job of building Machine Learning into its platform. Its Intelligence Cloud enables companies to gain meaningful, actionable insights and enables rapid experimentation. Further, on Optimizely's product roadmap is the ability to allow brands' data science and engineering teams to configure their own machine learning algorithms so that these brands can garner even better insights to optimize the digital experience.
Most DXPs like Acquia / Sitecore / Adobe Experience Manager have their own similar offerings or are in the process of playing catch-up to build their own suite of AI/ML tools. This typically revolves around the ability to do some level of basic prediction, clustering and segmenting users (personalization and targeting), and providing recommendations — whether it’s which audience would work best for a given campaign or what the next best action would be.
- What makes a brand or marketing organization ready to embrace AI and ML?
Most brands today are ready to start with AI and ML. In general, because ML works through large data sets, you would want larger amounts of historical data to parse through to get the most benefit out of predictive capabilities. However, that’s not a hard-requirement for things like automation where ML can bring significant benefits with tasks that are often repetitive in nature.
Think about how many users visit your site every day — there are thousands or millions of sessions and events happening every day — this is information that is available to analyze over long periods of time. This data and information can be used to personalize, experiment, and predict customer behaviors today.
Most organizations are worried about the large investments in infrastructure, data science and engineering talent that is needed to make use of AI/ML but many of these features are part of DXP offerings right now — nothing else is needed.
- How can Genuine help an organization to implement these exciting technologies?
The first step is understanding what you have available in your platform and if it has the right tools necessary to enable the use of AI/ML. This is something that we help many of our clients understand so that you can take a crawl, walk, run approach to using AI/ML features of a platform.
We find the easiest place to start is personalization and experimentation. Those are two areas that can enable marketing teams to move fast and utilize insights to determine better ways to optimize experiences and increase engagement.
Data is typically all over the place, so you will want to invest time and effort into unifying data — creating a data lake to use across multiple sources for hyper-personalized experiences and automation is something to consider for large enterprise customers, but can be a bit simpler for smaller mid-market businesses. This step is an area that requires expertise and is not generally doable out of the box. However, you typically always see a great return for your investment.
Having a partner that understands how to unify your data — identifying areas that would benefit from automation and document where data silos exist — and integrate it with your DXP to make use of its insights and automation capabilities is crucial.
Want to learn more? Give us a call and let’s find out how we can help you start using these tools.